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Graph Cuts in Vision and Graphics: Theories and Applications
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| Venue: | “MATH. MODELS OF C.VISION: THE HANDBOOK”, EDTS. PARAGIOS, CHEN, FAUGERAS |
| Citations: | 37 - 2 self |
BibTeX
@MISC{Boykov_graphcuts,
author = {Yuri Boykov and Olga Veksler},
title = { Graph Cuts in Vision and Graphics: Theories and Applications},
year = {}
}
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Abstract
Combinatorial min-cut algorithms on graphs emerged as an increasingly useful tool for problems in vision. Typically, the use of graphcuts is motivated by one of the following two reasons. Firstly, graph-cuts allow geometric interpretation; under certain conditions a cut on a graph can be seen as a hypersurface in N-D space embedding the corresponding graph. Thus, many applications in vision and graphics use min-cut algorithms as a tool for computing optimal hypersurfaces. Secondly, graphcuts also work as a powerful energy minimization tool for a fairly wide class of binary and non-binary energies that frequently occur in early vision. In some cases graph cuts produce globally optimal solutions. More generally, there are iterative graph-cut based techniques that produce provably good approximations which (were empirically shown to) correspond to high-quality solutions in practice. Thus, another large group of applications use graph-cuts as an optimization technique for low-level vision problems based on global energy formulations. This chapter is intended as a tutorial illustrating these two aspects of graph-cuts in the context of problems in computer vision and graphics. We explain general theoretical properties that motivate the use of graph cuts, as well as, show their limitations.
Keyphrases
graph cut powerful energy minimization tool early vision low-level vision problem corresponding graph large group certain condition optimization technique many application computer vision optimal solution non-binary energy graph-cuts allow geometric interpretation wide class good approximation n-d space optimal hypersurfaces min-cut algorithm global energy formulation combinatorial min-cut algorithm iterative graph-cut general theoretical property useful tool high-quality solution
